Opportunities and Challenges of Promoting Integrated Care Through Digitalisation–learning Lessons from Large-Scale National Programmes in England
HEALTH POLICY AND TECHNOLOGY(2024)
Abstract
There is a growing global interest in integrating health and care through digitalisation. However, many ambitious digitalisation initiatives in the healthcare sector fail to achieve their intended outcomes. One contributing factor is the failure to apply lessons learned from past endeavours. We here leverage the experiences gained from large-scale digitalisation efforts within the National Health Service (NHS) in England to distil valuable insights for strategic decision-makers who are embarking on the development and implementation of initiatives aimed at integrating health and social care through digitalisation. While not exhaustive, our compilation of eight key lessons serves as a foundational resource to inform such initiatives, seeking ultimately to contribute to realising maximum benefits for health and care organisations and service users.
MoreTranslated text
Key words
Digitalisation,Healthcare,Social care,Programmes
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2011
被引用276 | 浏览
2015
被引用149 | 浏览
2018
被引用49 | 浏览
2021
被引用74 | 浏览
2023
被引用3 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest